# Unsupervised Learning: Comparative Analysis of Clustering Techniques on High-Dimensional Data

arXiv: https://arxiv.org/abs/2503.23215 · HTML: https://mlsanity.com/paper/2503.23215/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 28 references that are in the map.
- First posted on arXiv 29 Mar 2025.

## Builds on (most cited first, 1 of 1)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)

## Built on by (most cited first, 1 of 1)

- [CARDIE: clustering algorithm on relevant descriptors for image enhancement](https://mlsanity.com/paper/2509.06116/) (2025)

## Abstract (arXiv)

This paper presents a comprehensive comparative analysis of prominent clustering algorithms K-means, DBSCAN, and Spectral Clustering on high-dimensional datasets. We introduce a novel evaluation framework that assesses clustering performance across multiple dimensionality reduction techniques (PCA, t-SNE, and UMAP) using diverse quantitative metrics. Experiments conducted on MNIST, Fashion-MNIST, and UCI HAR datasets reveal that preprocessing with UMAP consistently improves clustering quality across all algorithms, with Spectral Clustering demonstrating superior performance on complex manifold structures. Our findings show that algorithm selection should be guided by data characteristics, with Kmeans excelling in computational efficiency, DBSCAN in handling irregular clusters, and Spectral Clustering in capturing complex relationships. This research contributes a systematic approach for evaluating and selecting clustering techniques for high dimensional data applications.